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Found 17 papers, 5 papers with code
Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models
Large language models (LLMs) have exhibited impressive performance in language comprehension and various reasoning tasks.
LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models
This paper presents a comprehensive survey of the current status and opportunities for Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning that necessitates understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly.
K-Level Reasoning with Large Language Models
While Large Language Models (LLMs) have demonstrated their proficiency in complex reasoning tasks, their performance in dynamic, interactive, and competitive scenarios - such as business strategy and stock market analysis - remains underexplored.
Empirical Study of Large Language Models as Automated Essay Scoring Tools in English Composition__Taking TOEFL Independent Writing Task for Example
The primary objective is to assess the capabilities and constraints of ChatGPT, a prominent representative of large language models, within the context of automated essay scoring.
ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents
This paper introduces Alympics (Olympics for Agents), a systematic simulation framework utilizing Large Language Model (LLM) agents for game theory research.
EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form Narrative Text Generation
In this paper, we propose a new framework called Evaluation-guided Iterative Plan Extraction for long-form narrative text generation (EIPE-text), which extracts plans from the corpus of narratives and utilizes the extracted plans to construct a better planner.
Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration
In this work, we propose Solo Performance Prompting (SPP), which transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas.
Assessing Phrase Break of ESL Speech with Pre-trained Language Models and Large Language Models
To leverage NLP models, speech input is first force-aligned with texts, and then pre-processed into a token sequence, including words and phrase break information.
End-to-End Word-Level Pronunciation Assessment with MASK Pre-training
Pronunciation assessment is a major challenge in the computer-aided pronunciation training system, especially at the word (phoneme)-level.
Smart Word Suggestions for Writing Assistance
The training dataset comprises over 3. 7 million sentences and 12. 7 million suggestions generated through rules.
Low-code LLM: Graphical User Interface over Large Language Models
By introducing this framework, we aim to bridge the gap between humans and LLMs, enabling more effective and efficient utilization of LLMs for complex tasks.
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain-specific tasks very well.
Extensible Prompts for Language Models on Zero-shot Language Style Customization
We propose eXtensible Prompt (X-Prompt) for prompting a large language model (LLM) beyond natural language (NL).
Assessing Phrase Break of ESL speech with Pre-trained Language Models
The token sequence is then fed into the pre-training and fine-tuning pipeline.
Ordinal Regression via Binary Preference vs Simple Regression: Statistical and Experimental Perspectives
The ORARS addresses the MOS prediction problem by pairing a test sample with each of the pre-scored anchored reference samples.
An Approach to Mispronunciation Detection and Diagnosis with Acoustic, Phonetic and Linguistic (APL) Embeddings
These embeddings, when used as implicit phonetic supplementary information, can alleviate the data shortage of explicit phoneme annotations.
Improving pronunciation assessment via ordinal regression with anchored reference samples
Traditional speech pronunciation assessment, based on the Goodness of Pronunciation (GOP) algorithm, has some weakness in assessing a speech utterance: 1) Phoneme GOP scores cannot be easily translated into a sentence score with a simple average for effective assessment; 2) The rank ordering information has not been well exploited in GOP scoring for delivering a robust assessment and correlate well with a human rater's evaluations.
